Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks

ArXi:2510.12635v3 Announce Type: replace Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that treats working memory management as learnable policy actions.